Best Practices in Trade Based Money Laundering Prevention

Next Generation Transaction Monitoring

O R A C L E W H I T E P A P E R | J U L Y 2 0 1 6

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Table of Contents

About Trade-based Money Laundering (TBML) 2

Critical Success Factor: Good Data Governance 3

The Usage of Data Analytics to Identify TBML 4

Next Generation TBML Monitoring 6

The Evolution of TBML Monitoring in Banks 6

References 8

About the Author

Phil Malcolm,

Leader, Financial Crime and Compliance, JAPAC, Oracle Financial Services Phil’s role is primarily one of liaison between Oracle and the Regulators, Oracle Partners, Market Influencers and Oracle clients in the JAPAC region. Phil has been involved with Compliance software for over 15 years. Prior to joining Oracle Phil was with SunGard where he was the Regional Sales Manager. Phil covered areas such as Market Abuse, Broker Compliance, Inside Trading, Customer On-boarding and Personal Account Dealing. Prior to SunGard Phil worked with the two main credit bureaus in Australia helping FI’s implement the first round of straight through KYC processing of new customers using electronic verification. Email: phil.malcolm@oracle.com

2 | THE NEW AGE TRANSACTION MONITORING FOR TRADE BASED MONEY LAUNDERING

About Trade-based Money Laundering (TBML)

Trade-based money laundering (TBML) is a form of laundering where criminal organizations use trade finance

to launder money by using trade of goods instead of an actual monetary transaction. Hundreds of billions of

dollars are laundered annually through TBML, according to an International Narcotics Control Board strategy

report1. The Global Financial Integrity report (Dec 2015)2 specified that fraudulent misinvoicing of trade

transactions constituted the largest component (83.4 percent) of illicit financial flows from developing countries.

Additionally, the US Department of Treasury, in its 2015 National Anti-Money Laundering Risk Assessment3

stated that TBML can have a more destructive impact on legitimate commerce than other money laundering

activities.

The Committee for the Global Financial System has estimated the total

value of bank-intermediated trade finance in 2014 to be between

US$6.5 trillion and US$8 trillion4. Indeed, banks are the central house

for all financial transactions pertaining to trade finance. They have

access to the complete documentations and profiles of their customers

and are therefore best placed to police TBML activities. It is thus no

surprise that law enforcement agencies and regulators who are keen

to clamp down on TBML activities have shifted their focus onto banks.

However, despite banks being ideally placed to police TBML, they face

many challenges in this endeavor. As trade finance is an extremely document-centric process, there is

considerable reliance on manual screening where the document checker’s experience comes into play. In

addition, customers are often reluctant to share relevant information that will enable them to do compliance

monitoring. Compound this with the fact that information is often provided in an unstructured data format (i.e. bill

of lading) or semi-structured data format (i.e. SWIFT messages) and the inherent difficulties of automated

monitoring are apparent.

Key Focus for Managing TBML-related Risks

Several major factors contribute to the risks inherent in TBML and also the challenges in monitoring those risks.

These include:

• Involvement of multiple parties on both sides of the trade

• Document-centric nature of TBML – more so than with other banking activities

• Misrepresenting the value of goods

• Collusion between buyers and sellers

• Disguising the identity of the applicant, using shell companies or offshore front companies

The total value of bank-intermediated trade finance is between US$6.5 trillion and US$8 trillion…it is thus no surprise that law enforcement agencies and regulators keen to clamp down on TBML activities have shifted their focus onto banks.

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The Bank Secrecy Act (BSA) recommends that to institute effective money laundering controls for trade finance,

banks should adopt more rigor in the following areas:

• Customer due diligence

• Transaction Screening

• Transaction Monitoring

• Suspicious Activity Reports (SARs)

Another important aspect to consider in trade finance controls is document fraud. Owing to the complexity

involved in trade-related activities, using manual screening as a supplement of automated systems is essential.

Critical Success Factor: Good Data Governance

A sound approach to data preparation is critical to preventing TBML within banks. However, the challenge here

is that the volume, variety and quality of data can vary based on operational processes as well as the level of

automation within the organization. As such, risk and compliance managers need to conduct an assessment of

the availability and quality of the data, in tandem with the owners of systems and processes as well as the relevant

IT teams supporting the systems.

Data provided by the customer that needs to be reviewed falls into several categories:

• The customer’s company information

• Trade finance contract types

• Information about the various contract parties

• Information about the contracted goods and services

• Details of the contract document

• Shipment details

• Details of payment transactions

• SWIFT messages

In addition to the above, external data also needs to be screened. This can include information about high risk

goods, vessel information, the company’s background information such as shareholders and profile and

information about contract parties.

From a bank’s perspective, there are other important questions which need to be answered - Has there been any

negative news about the company/customer? Is the company operating from a sanctioned country list or a high

risk country list?

Here, there is also an opportunity for banks, import/export companies, customs authorities and other

organizations in the trade finance value chain to collaborate and share information, and a potential need to create

trade transparency units (TTUs) 5.

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The Usage of Data Analytics to Identify TBML

There are a range of traditional analytical techniques that banks currently employ to identify money laundering

activities which can also be applied to monitor TBML:

Rule engines – checks on the volume, velocity and variance in transactions, which can be modeled as

business rules. These rules can also be layered with techniques such as profiling, trends, and text

analysis to detect suspicious/unusual activities.

Profile and trend analysis – analyzing data sources to construct statistics and summaries about

expected behaviors, which can then be used to identify deviations from the norm.

Sequence mining – a form of data mining where information appears in a sequence (i.e. shipment

requests). Multiple shipment requests from a customer can be checked to see if the party is using a

sequential number of containers – an indication of fraud (i.e. non-existent containers.)

Link analysis – a technique used to identify hidden relationships with unrelated parties, by detecting

common links between them, such as a specific individual or company.

The problem with the above mentioned traditional AML monitoring techniques is that they are designed to work

on standard, curated and accurate datasets, but would fail when it comes to monitoring semi-structured or

unstructured trade finance information.

Some of the newer analytical techniques to analyze semi-structured or unstructured data include:

Text analytics/text mining – this involves the use of natural language processing, and statistical and

machine learning techniques to model and structure information from textual sources to derive high

quality information.

Big data analytics – Big data is usually characterized by one or more of the following: high volume,

high velocity or high variety. Given that trade finance

data comes from a variety of internal and external

sources, and given their unstructured format, big data

analytics brings versatility to the data consumption

process.

Creation of a Trade Reference database – Having a

library of industries, products, channels, vessel names

etc will bring greater accuracy to monitoring and

efficiency to analysis of the money laundering activity.

Statistical analytics – a process of quantitatively describing the main features in a dataset, which can,

through mathematical models, be compared against global or regional benchmarks for congruence (or

the lack of it).

Visual and predictive analytics – advanced statistical models can help banks anticipate possible future

outcomes.

Web analytics – web crawling software can be used to download relevant web pages matching specific

search terms and criteria, (i.e. container details, negative news) to indicate TBML acts.

Image analytics – converting hardcopy documents to consumable formats to reduce the effort on

gathering and structuring data.

Big data is usually characterized by high volume, high velocity or high variety.

Given that trade finance business data comes via a variety of sources, and given

their unstructured format, big data analytics has a definite role to play.

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Figure 1 below provides a perspective on the complexities in information sources involved as well as traditional

and new analytical techniques which can be applied on this data to derive meaningful insights.

Figure1. Data Analytics for Trade-based Money Laundering Monitoring

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Next Generation TBML Monitoring

Many of the trade finance departments within banks currently still rely on manual screening, due to the

unavailability of proven technologies as well as cost issues in automation. One of the primary issues in adopting

a unified data approach is that the data sits on multiple silos across the bank, and even outside the bank. Sharing

data is often not possible without manual interventions and this impedes the bank’s ability to aggregate data to

meet compliance and regulatory requirements. This makes

automating the transaction monitoring process not a

straightforward process.

The fact that data is often not available in a consolidated

fashion is a major issue for banks. Data can either be

available in a structured, unstructured or in a semi-

structured format. For example, when it comes to SWIFT messages (semi-structured), there could be any of a

number of issues, from multiple information stored in single fields, to poor spellings to non-standardized

information to ambiguous phraseologies and others.

When it comes to unstructured data, while it can be quite easily obtained, it is held across various organizations

and in various forms – i.e. revenue departments, customs departments, accountancy data, general financial data,

market data, topography data, the Internet, and social media. Given that these sources of data are not integrated,

coupled with the sources being third party providers, the challenge of aggregating and preparing data is a

mammoth task. In the absence of automated technologies, 80% of the effort is likely to be spent on preparing

the data versus the analytics.

For banks to undertake TBML monitoring, they need to invest in instituting an appropriate architecture. This

includes:

• Collection of data from various sources

• Enhancing source systems to capture data and conduct pre-transaction screening

• Data quality governance and management

• Automated monitoring of TBML red flags using various analytical techniques

• Enterprise wide case management

• Investigative analytics

• Management reporting and SAR filing

For TBML monitoring to be effective, automation needs to be supplemented with strong operational processes.

The Evolution of TBML Monitoring in Banks

While the benefits of a fully automated TBML monitoring process are overwhelming, it may be too ambitious a

goal for banks to dive straight into, especially if they have legacy systems and their data management processes

have not evolved.

Banks can instead opt to take incremental steps towards automation, through progressive advancements in each

of the key areas – processes, screening, data, analytics and knowledge investment. This is represented in the

TBML Monitoring Maturity Model6 shown in Figure 2.

At the Limited phase in the maturity spectrum of TBML monitoring, processes are likely to be highly manual,

and characterized by inconsistent screening practices. The data required for TBML monitoring is either

unavailable or disparate across the bank’s siloed systems and infrastructure. The bank does not have analytical

In the absence of automated technologies, 80% of the effort is likely to be spent on preparing the data versus the analytics.

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capabilities nor a dedicated trade finance compliance department, and there is heavy reliance on the first line of

defense – the document checkers.

There are many banks that have recognized the benefits of automating the TBML monitoring, and are beginning

to implement relevant technologies and processes. These banks, which are in the Core phase in the TBML

monitoring maturity spectrum, have achieved a certain degree of automation and have instituted pre-transaction

screening, they are still partially dependent on manual document checking, and their analytical capabilities are

descriptive at best. Importantly, the quality of their data is lacking and potentially conflicting.

Figure 2. Trade-based Money Laundering Monitoring Maturity Model

Banks in the Enhanced phase of the TBML monitoring maturity spectrum have implemented full automation of

their TBML processes. Post-transaction screening allows for a more comprehensive monitoring process,

throughout the entire trade finance lifecycle. While trade finance data is more enriched, they are still disparately

located across department silos within the organization. Banks at this stage will have put in place TBML team,

albeit with heavy reliance on technologies, and partial reliance on a first line of defense. The bank’s reliance on

technology allows it to run diagnostic analytics of its data.

A bank can only consider itself at the Leading Phase of TBML monitoring when it has the following – trade

finance data that is consolidated across the entire enterprise, allowing it to run predictive analytics capabilities

and screening takes place both pre- and post-transaction as well as across the entire trade finance lifecycle.

Finally, there is a specialized TBML team in place, and the bank has succeeded in achieving an optimum balance

between people, processes and technology.

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Summary Success in TBML monitoring is underpinned by collaboration with multiple parties, internally within the bank, as

well as externally, to achieve transparency. The good news is that the process of digitization in trade finance is

already well underway in the banking industry, with technologies such as blockchain, electronic bills of lading

and electronic issuance replacing what once were manual processes.

In addition, new data analytic techniques are emerging, such as big data discovery, text, statistic, web, visual

and predictive analytics augmenting proven techniques such as rules engines, profiling/trend analysis, sequence

mining and link analysis.

This will provide banks with the ability to derive a markedly higher quality of insight as well as more timely and

accurate alerts. All these can only help banks in their efforts to put in place the necessary processes, controls,

data governance and automation to achieve regulatory compliance in the highly complex field of trade based

money laundering prevention.

References

1 Advancing Financial Crimes Professionals Worldwide, “Trade Based Money Laundering”. [Online]: http://www.acams.org/aml-resources/trade-

based-money-laundering/ 2 Global Financial Integrity (GFI), Illicit Financial Flows from Developing Countries: 2004-2013. [Online]: http://www.gfintegrity.org/wp-

content/uploads/2015/12/IFF-Update_2015-Final.pdf 3 US Department of Treasury, 2015 National Anti-Money Laundering Risk Assessment. [Online]: https://www.treasury.gov/resource-center/terrorist-

illicit-finance/Documents/National%20Money%20Laundering%20Risk%20Assessment%20%E2%80%93%2006-12-2015.pdf 4 Bank for International Settlements, “Trade finance: developments and issues,” by the Committee on the Global Financial System, January 2014.

[Online]: http://www.bis.org/publ/cgfs50.pdf 5 Trade Based Money Laundering: The Next Frontier of International Money Laundering Enforcement. John A. Cassara © 2016 by John A. Cassara.

Published 2016 by John Wiley & Sons Inc. 6 Manisha Khanna, Combating Trade-Based Money Laundering in the Asia Pacific Region. ACAMS 8th Annual AML and Financial Crimes Conference

Asia Pacific, April 2016

Phil Malcolm I phil.malcolm@oracle.com I +61 414 356 138

Copyright © 2016, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only, and the

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